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Kucewicz MT, Cimbalnik J, Garcia-Salinas JS, Brazdil M, Worrell GA. High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams? Brain 2024; 147:2966-2982. [PMID: 38743818 PMCID: PMC11370809 DOI: 10.1093/brain/awae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.
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Affiliation(s)
- Michal T Kucewicz
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Bioelectronics, Neurophysiology and Engineering Laboratory, Mayo Clinic, Departments of Neurology and Biomedical Engineering & Physiology, Mayo Clinic, Rochester, MN 55902, USA
| | - Jan Cimbalnik
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Department of Biomedical Engineering, St. Anne’s University Hospital in Brno & International Clinical Research Center, Brno 602 00, Czech Republic
- Brno Epilepsy Center, 1th Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, member of the ERN-EpiCARE, Brno 602 00, Czech Republic
| | - Jesus S Garcia-Salinas
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
| | - Milan Brazdil
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Brno Epilepsy Center, 1th Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, member of the ERN-EpiCARE, Brno 602 00, Czech Republic
- Behavioural and Social Neuroscience Research Group, CEITEC—Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - Gregory A Worrell
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Bioelectronics, Neurophysiology and Engineering Laboratory, Mayo Clinic, Departments of Neurology and Biomedical Engineering & Physiology, Mayo Clinic, Rochester, MN 55902, USA
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Doss DJ, Shless JS, Bick SK, Makhoul GS, Negi AS, Bibro CE, Rashingkar R, Gummadavelli A, Chang C, Gallagher MJ, Naftel RP, Reddy SB, Williams Roberson S, Morgan VL, Johnson GW, Englot DJ. The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy. Brain 2024; 147:3009-3017. [PMID: 38874456 PMCID: PMC11370787 DOI: 10.1093/brain/awae189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 04/19/2024] [Accepted: 05/16/2024] [Indexed: 06/15/2024] Open
Abstract
Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.
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Affiliation(s)
- Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Jared S Shless
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Sarah K Bick
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Ghassan S Makhoul
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Aarushi S Negi
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Camden E Bibro
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Rohan Rashingkar
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Martin J Gallagher
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Shilpa B Reddy
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA
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Akamine IR, Garich JV, Gulick DW, Hara SA, Benscoter MA, Kuehn ST, Worrell GA, Raupp GB, Blain Christen JM. Development of a novel, concentric micro-ECoG array enabling simultaneous detection of a single location by multiple electrode sizes. Biomed Phys Eng Express 2024; 10:045040. [PMID: 38744259 DOI: 10.1088/2057-1976/ad4b1c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 05/14/2024] [Indexed: 05/16/2024]
Abstract
Objective.Detection of the epileptogenic zone is critical, especially for patients with drug-resistant epilepsy. Accurately mapping cortical regions exhibiting high activity during spontaneous seizure events while detecting neural activity up to 500 Hz can assist clinicians' surgical decisions and improve patient outcomes.Approach.We designed, fabricated, and tested a novel hybrid, multi-scale micro-electrocorticography (micro-ECoG) array with a unique embedded configuration. This array was compared to a commercially available microelectrode array (Neuronexus) for recording neural activity in rodent sensory cortex elicited by somatosensory evoked potentials and pilocarpine-induced seizures.Main results.Evoked potentials and spatial maps recorded by the multi-scale array ('micros', 'mesos', and 'macros' refering to the relative electrode sizes, 40 micron, 1 mm, and 4 mm respectively) were comparable to the Neuronexus array. The SSEPs recorded with the micros had higher peak amplitudes and greater signal power than those recorded by the larger mesos and macro. Seizure onset events and high-frequency oscillations (∼450 Hz) were detected on the multi-scale, similar to the commercially available array. The micros had greater SNR than the mesos and macro over the 5-1000 Hz frequency range during seizure monitoring. During cortical stimulation experimentation, the mesos successfully elicited motor effects.Significance.Previous studies have compared macro- and microelectrodes for localizing seizure activity in adjacent regions. The multi-scale design validated here is the first to simultaneously measure macro- and microelectrode signals from the same overlapping cortical area. This enables direct comparison of microelectrode recordings to the macroelectrode recordings used in standard neurosurgical practice. Previous studies have also shown that cortical regions generating high-frequency oscillations are at an increased risk for becoming epileptogenic zones. More accurate mapping of these micro seizures may improve surgical outcomes for epilepsy patients.
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Affiliation(s)
- Ian R Akamine
- Biomedical & Health Systems Engineering, Arizona State University, Tempe, AZ, United States of America
| | - Jonathan V Garich
- Biomedical & Health Systems Engineering, Arizona State University, Tempe, AZ, United States of America
- Division of Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Daniel W Gulick
- Electrical, Computer, & Energy Engineering, Arizona State University, Tempe, AZ, United States of America
| | - Seth A Hara
- Division of Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Mark A Benscoter
- Division of Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Stephen T Kuehn
- Division of Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Gregory A Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Gregory B Raupp
- Engineering of Matter, Transport, & Energy, Arizona State University, Tempe, AZ, United States of America
| | - Jennifer M Blain Christen
- Electrical, Computer, & Energy Engineering, Arizona State University, Tempe, AZ, United States of America
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Cai Z, Jiang X, Bagić A, Worrell GA, Richardson M, He B. Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592202. [PMID: 38746136 PMCID: PMC11092614 DOI: 10.1101/2024.05.02.592202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence Summary Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
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Medvedev AV, Lehmann B. The detection of absence seizures using cross-frequency coupling analysis with a deep learning network. RESEARCH SQUARE 2024:rs.3.rs-4178484. [PMID: 38659733 PMCID: PMC11042430 DOI: 10.21203/rs.3.rs-4178484/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
High frequency oscillations are important novel biomarkers of epileptogenic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to recognize absence seizure activity based on the cross-frequency patterns within scalp EEG. We used EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Half of the records were selected randomly for network training and the second half were used for testing. Power-to-power coupling was calculated between all frequencies 2-120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 96.3%, specificity of 99.8% and overall accuracy of 98.5%. Our results provide evidence that the SSAE neural networks can be used for automated detection of absence seizures within scalp EEG.
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Navas-Olive A, Rubio A, Abbaspoor S, Hoffman KL, de la Prida LM. A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species. Commun Biol 2024; 7:211. [PMID: 38438533 PMCID: PMC10912113 DOI: 10.1038/s42003-024-05871-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/29/2024] [Indexed: 03/06/2024] Open
Abstract
The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.
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Affiliation(s)
| | | | - Saman Abbaspoor
- Psychological Sciences, Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Kari L Hoffman
- Psychological Sciences, Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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Paulk AC, Salami P, Zelmann R, Cash SS. Electrode Development for Epilepsy Diagnosis and Treatment. Neurosurg Clin N Am 2024; 35:135-149. [PMID: 38000837 DOI: 10.1016/j.nec.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
Recording neural activity has been a critical aspect in the diagnosis and treatment of patients with epilepsy. For those with intractable epilepsy, intracranial neural monitoring has been of substantial importance. Clinically, however, methods for recording neural information have remained essentially unchanged for decades. Over the last decade or so, rapid advances in electrode technology have begun to change this landscape. New systems allow for the observation of neural activity with high spatial resolution and, in some cases, at the level of the activity of individual neurons. These new tools have contributed greatly to our understanding of brain function and dysfunction. Here, the authors review the primary technologies currently in use in humans. The authors discuss other possible systems, some of the challenges which come along with these devices, and how they will become incorporated into the clinical workflow. Ultimately, the expectation is that these new, high-density, high-spatial-resolution recording systems will become a valuable part of the clinical arsenal used in the diagnosis and surgical management of epilepsy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA.
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Rina Zelmann
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
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Ardelean ER, Bârzan H, Ichim AM, Mureşan RC, Moca VV. Sharp detection of oscillation packets in rich time-frequency representations of neural signals. Front Hum Neurosci 2023; 17:1112415. [PMID: 38144896 PMCID: PMC10748759 DOI: 10.3389/fnhum.2023.1112415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.
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Affiliation(s)
- Eugen-Richard Ardelean
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
| | - Harald Bârzan
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Ana-Maria Ichim
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Raul Cristian Mureşan
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania
| | - Vasile Vlad Moca
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
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Shin HJ, Kim SH, Kang HC, Lee JS, Kim HD. Surgical Treatment of Epilepsy with Bilateral MRI Abnormalities. World Neurosurg 2023; 180:e37-e45. [PMID: 37495100 DOI: 10.1016/j.wneu.2023.07.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVE To investigate the surgical outcomes of patients with drug-resistant epilepsy and bilateral brain magnetic resonance imaging (MRI) abnormalities who had undergone various epilepsy surgeries. METHODS Patients with drug-resistant epilepsy and bilateral brain abnormalities on MRI who underwent epilepsy surgery at the Severance Children's Hospital between October 2003 and December 2021 were included. The age of seizure onset was 18 years or younger. Engel's classification was used to assess seizure outcomes at 1, 2, and 5 years after surgery. RESULTS A total of 40 patients met the inclusion criteria. The median age at surgery was 10.9 years (interquartile range [IQR] 6.9-15.1); the median interval to surgery was 7.1 years (IQR 2.7-11.5). One year after surgery, a favorable outcome of Engel class I-II was observed in 53% (21/40) of patients. At the 2- and 5-year follow-ups, 56% (20/36) and 63% (17/27) of patients showed good postoperative outcomes, respectively. CONCLUSIONS Approximately, half of the patients with bilateral brain MRI abnormalities achieved seizure freedom after epilepsy surgery. The existence of bilateral brain MRI abnormalities should not hinder resective epilepsy surgery.
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Affiliation(s)
- Hui Jin Shin
- Division of Pediatric Neurology, Department of Pediatrics, Epilepsy Research Institute, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hee Kim
- Division of Pediatric Neurology, Department of Pediatrics, Epilepsy Research Institute, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hoon-Chul Kang
- Division of Pediatric Neurology, Department of Pediatrics, Epilepsy Research Institute, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Joon Soo Lee
- Division of Pediatric Neurology, Department of Pediatrics, Epilepsy Research Institute, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Heung Dong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Epilepsy Research Institute, Severance Children's Hospital, Yonsei University College of Medicine, Seoul, Korea; Department of Pediatrics, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Xia L, Feng Y, Guo Z, Ding J, Li Y, Li Y, Ma M, Gan G, Xu Y, Luo J, Shi Z, Guan Y. MuLHiTA: A Novel Multiclass Classification Framework With Multibranch LSTM and Hierarchical Temporal Attention for Early Detection of Mental Stress. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:9657-9670. [PMID: 35385389 DOI: 10.1109/tnnls.2022.3159573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Mental stress is an increasingly common psychological issue leading to diseases such as depression, addiction, and heart attack. In this study, an early detection framework based on electroencephalogram (EEG) data is developed for reducing the risk of these diseases. In existing frameworks, signals are often segmented into smaller sections prior to being input to a deep neural network. However, this approach ignores the fundamental nature of EEG signals as a carrier of valuable information (e.g., the integrity of frequency and phase, and temporal fluctuations of EEG components). As such, this type of segmenting may lead to information loss and a failure to effectively identify mental stress levels. Thus, we propose a novel multiclass classification framework termed multibranch LSTM and hierarchical temporal attention (MuLHiTA) for the early identification of mental stress levels. It specifically focuses on not only intraslice (within each slice) but also interslice (between different slices) samples in parallel. This was achieved by including two complementary branches, each of which integrated a specifically designed attention module into a bidirectional long short-term memory (BLSTM) network, enabling extraction of the most discriminative features from interslice and intraslice EEG signals simultaneously. The outputs of attention modules were then summed to obtain a feature representation that contributes to reduce overfitting and more effective multiclass classification. In addition, electrode positions were optimized using neural activity areas under high-stress conditions, thereby reducing computational costs by minimizing the number of critical electrodes. MuLHiTA was evaluated across one private [Montreal imaging stress task (MIST)] and two publicly available EEG datasets [EEG during mental arithmetic tasks (DMAT) and Simultaneous task EEG workload (STEW)]. These were divided into training and test sets using an 8:2 ratio, and the training data were further divided into training and validation sets using a fivefold cross-validation (CV) method, in which the model with the highest accuracy among the five was selected. The model was trained once more with the full training set, and the test data were then used to evaluate its performance. This approach achieved average classification accuracies of 93.58%, 91.80%, and 99.71% for the MIST, STEW, and DMAT datasets, respectively. Experimental results showed MuLHiTA was superior to state-of-the-art algorithms, including EEGNet, BLSTM, EEGLearn, convolutional neural network (CNN)-long short-term memory (LSTM), and convolutional recurrent attention model (CRAM), for multiclass classification. This demonstrates the viability of MuLHiTA for the early detection of mental stress.
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Revajová K, Trávníček V, Jurák P, Vašíčková Z, Halámek J, Klimeš P, Cimbálník J, Brázdil M, Pail M. Interictal invasive very high-frequency oscillations in resting awake state and sleep. Sci Rep 2023; 13:19225. [PMID: 37932365 PMCID: PMC10628183 DOI: 10.1038/s41598-023-46024-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/26/2023] [Indexed: 11/08/2023] Open
Abstract
Interictal very high-frequency oscillations (VHFOs, 500-2000 Hz) in a resting awake state seem to be, according to a precedent study of our team, a more specific predictor of a good outcome of the epilepsy surgery compared to traditional interictal high-frequency oscillations (HFOs, 80-500 Hz). In this study, we retested this hypothesis on a larger cohort of patients. In addition, we also collected patients' sleep data and hypothesized that the occurrence of VHFOs in sleep will be greater than in resting state. We recorded interictal invasive electroencephalographic (iEEG) oscillations in 104 patients with drug-resistant epilepsy in a resting state and in 35 patients during sleep. 21 patients in the rest study and 11 patients in the sleep study met the inclusion criteria (interictal HFOs and VHFOs present in iEEG recordings, a surgical intervention and a postoperative follow-up of at least 1 year) for further evaluation of iEEG data. In the rest study, patients with good postoperative outcomes had significantly higher ratio of resected contacts with VHFOs compared to HFOs. In sleep, VHFOs were more abundant than in rest and the percentage of resected contacts in patients with good and poor outcomes did not considerably differ in any type of oscillations. In conclusion, (1) our results confirm, in a larger patient cohort, our previous work about VHFOs being a specific predictor of the area which needs to be resected; and (2) that more frequent sleep VHFOs do not further improve the results.
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Affiliation(s)
- Karin Revajová
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic.
| | - Vojtěch Trávníček
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Pavel Jurák
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
| | - Zuzana Vašíčková
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Josef Halámek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
| | - Petr Klimeš
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Jan Cimbálník
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Milan Brázdil
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- Central European Institute of Technology, Masaryk University, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
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12
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy. Clin Neurophysiol 2023; 154:129-140. [PMID: 37603979 PMCID: PMC10861270 DOI: 10.1016/j.clinph.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - William Speier
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA.
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13
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Coughlin B, Muñoz W, Kfir Y, Young MJ, Meszéna D, Jamali M, Caprara I, Hardstone R, Khanna A, Mustroph ML, Trautmann EM, Windolf C, Varol E, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Mark Richardson R, Williams ZM, Cash SS, Paulk AC. Modified Neuropixels probes for recording human neurophysiology in the operating room. Nat Protoc 2023; 18:2927-2953. [PMID: 37697108 DOI: 10.1038/s41596-023-00871-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/08/2023] [Indexed: 09/13/2023]
Abstract
Neuropixels are silicon-based electrophysiology-recording probes with high channel count and recording-site density. These probes offer a turnkey platform for measuring neural activity with single-cell resolution and at a scale that is beyond the capabilities of current clinically approved devices. Our team demonstrated the first-in-human use of these probes during resection surgery for epilepsy or tumors and deep brain stimulation electrode placement in patients with Parkinson's disease. Here, we provide a better understanding of the capabilities and challenges of using Neuropixels as a research tool to study human neurophysiology, with the hope that this information may inform future efforts toward regulatory approval of Neuropixels probes as research devices. In perioperative procedures, the major concerns are the initial sterility of the device, maintaining a sterile field during surgery, having multiple referencing and grounding schemes available to de-noise recordings (if necessary), protecting the silicon probe from accidental contact before insertion and obtaining high-quality action potential and local field potential recordings. The research team ensures that the device is fully operational while coordinating with the surgical team to remove sources of electrical noise that could otherwise substantially affect the signals recorded by the sensitive hardware. Prior preparation using the equipment and training in human clinical research and working in operating rooms maximize effective communication within and between the teams, ensuring high recording quality and minimizing the time added to the surgery. The perioperative procedure requires ~4 h, and the entire protocol requires multiple weeks.
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Affiliation(s)
- Brian Coughlin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - William Muñoz
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Michael J Young
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Domokos Meszéna
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mohsen Jamali
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Irene Caprara
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Richard Hardstone
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Arjun Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, USA
| | - Charlie Windolf
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Erdem Varol
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Computer Science and Engineering, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Sergey D Stavisky
- Department of Neurological Surgery, University of California Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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14
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Navas-Olive A, Rubio A, Abbaspoor S, Hoffman KL, de la Prida LM. A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.02.547382. [PMID: 37461661 PMCID: PMC10349962 DOI: 10.1101/2023.07.02.547382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
The study of sharp-wave ripples (SWRs) has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy and Alzheimer's disease is considered a biomarker of dysfunction. SWRs exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine learning (ML) models for automatic detection and analysis of SWRs. The ML architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of SWR features recorded in the dorsal hippocampus of mice. When applied to data from the macaque hippocampus, these models were able to generalize detection and revealed shared SWR properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize SWR research, lowering the threshold for its adoption in biomedical applications.
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Affiliation(s)
| | | | - Saman Abbaspoor
- Psychological Sciences, Vanderbilt Brain Institute, Vanderbilt University, USA
| | - Kari L. Hoffman
- Psychological Sciences, Vanderbilt Brain Institute, Vanderbilt University, USA
- Biomedical Engineering, Vanderbilt University, USA
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15
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Feng Y, Chang P, Kang Y, Liao P, Li CY, Liu J, Zhang WS. Etomidate-Induced Myoclonus in Sprague-Dawley Rats Involves Neocortical Glutamate Accumulation and N -Methyl- d -Aspartate Receptor Activity. Anesth Analg 2023; 137:221-233. [PMID: 36607803 DOI: 10.1213/ane.0000000000006292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Etomidate-induced myoclonus, a seizure-like movement, is of interest to anesthetists. However, its origin in the brain and its underlying mechanism remain unclear. METHODS Adult male Sprague-Dawley rats were anesthetized with etomidate, propofol, or lidocaine plus etomidate. We assessed the incidence of myoclonus, behavioral scores, and levels of glutamate and γ-aminobutyric acid (GABA) in the neocortex and hippocampus. To determine the origin and how N -methyl- d -aspartate receptors (NMDARs) modulate etomidate-induced neuroexcitability, the local field potential and muscular tension were monitored. Calcium imaging in vitro and immunoblotting in vivo were conducted to investigate the mechanisms underlying myoclonus. RESULTS The incidence of etomidate (1.5 mg/kg in vivo)-induced myoclonus was higher than that of propofol (90% vs 10%, P = .0010) and lidocaine plus etomidate (90% vs 20%, P = .0050). Etomidate at doses of 3.75 and 6 mg/kg decreased the mean behavioral score at 1 (mean difference [MD]: 1.80, 95% confidence interval [CI], 0.58-3.02; P = .0058 for both), 2 (MD: 1.60, 95% CI, 0.43-2.77; P = .0084 and MD: 1.70, 95% CI, 0.54-2.86; P = .0060), 3 (MD: 1.60, 95% CI, 0.35-2.85; P = .0127 and MD: 1.70, 95% CI, 0.46-2.94; P = .0091) minutes after administration compared to etomidate at a dose of 1.5 mg/kg. In addition, 0.5 and 1 µM etomidate in vitro increased neocortical intracellular calcium signaling; this signaling decreased when the concentration increased to 5 and 10 μM. Etomidate increased the glutamate level compared to propofol (mean rank difference: 18.20; P = .003), and lidocaine plus etomidate (mean rank difference: 21.70; P = .0002). Etomidate in vivo activated neocortical ripple waves and was positively correlated with muscular tension amplitude (Spearman's r = 0.785, P < .0001). Etomidate at 1.5 mg/kg decreased the K-Cl cotransporter isoform 2 (KCC2) level compared with propofol (MD: -1.15, 95% CI, -1.47 to -0.83; P < .0001) and lidocaine plus etomidate (MD: -0.64, 95% CI, -0.96 to -0.32; P = .0002), DL-2-amino-5-phosphopentanoic acid (AP5) suppressed these effects, while NMDA enhanced them. CONCLUSIONS Etomidate-induced myoclonus or neuroexcitability is concentration dependent. Etomidate-induced myoclonus originates in the neocortex. The underlying mechanism involves neocortical glutamate accumulation and NMDAR modulation and myoclonus correlates with NMDAR-induced downregulation of KCC2 protein expression.
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Affiliation(s)
- Yan Feng
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Pan Chang
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Yi Kang
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Ping Liao
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Chen-Yang Li
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Jin Liu
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Wen-Sheng Zhang
- From the Laboratory of Anesthesia and Critical Care Medicine, Department of Anesthesiology, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
- National-Local Joint Engineering Research Center of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
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16
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Barth KJ, Sun J, Chiang CH, Qiao S, Wang C, Rahimpour S, Trumpis M, Duraivel S, Dubey A, Wingel KE, Voinas AE, Ferrentino B, Doyle W, Southwell DG, Haglund MM, Vestal M, Harward SC, Solzbacher F, Devore S, Devinsky O, Friedman D, Pesaran B, Sinha SR, Cogan GB, Blanco J, Viventi J. Flexible, high-resolution cortical arrays with large coverage capture microscale high-frequency oscillations in patients with epilepsy. Epilepsia 2023; 64:1910-1924. [PMID: 37150937 PMCID: PMC10524535 DOI: 10.1111/epi.17642] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
OBJECTIVE Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. METHODS We used novel liquid crystal polymer thin-film μECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. RESULTS We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty-seven percent of HFOs occurred on single 200-μm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. SIGNIFICANCE These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.
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Affiliation(s)
- Katrina J. Barth
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - James Sun
- Center for Neural Science, New York University, New York, NY, USA
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shaoyu Qiao
- Center for Neural Science, New York University, New York, NY, USA
| | - Charles Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shervin Rahimpour
- Department of Neurosurgery, Clinical Neuroscience Center, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Agrita Dubey
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katie E. Wingel
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex E. Voinas
- Center for Neural Science, New York University, New York, NY, USA
| | | | - Werner Doyle
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, USA
| | - Derek G. Southwell
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Michael M. Haglund
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Matthew Vestal
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Stephen C. Harward
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Florian Solzbacher
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, USA
| | - Sasha Devore
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Orrin Devinsky
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, USA
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Comprehensive Epilepsy Center, NYU Langone Health, New York, NY, USA
| | - Daniel Friedman
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Saurabh R. Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gregory B. Cogan
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Justin Blanco
- Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD, USA
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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17
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288435. [PMID: 37131743 PMCID: PMC10153337 DOI: 10.1101/2023.04.13.23288435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Objective This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs). Methods We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. Results The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. Conclusions HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. Significance Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes. HIGHLIGHTS HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detectorHFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathologicalA deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods.
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18
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Sindhu KR, Ngo D, Ombao H, Olaya JE, Shrey DW, Lopour BA. A novel method for dynamically altering the surface area of intracranial EEG electrodes. J Neural Eng 2023; 20:026002. [PMID: 36720162 PMCID: PMC9990369 DOI: 10.1088/1741-2552/acb79f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 01/31/2023] [Indexed: 02/02/2023]
Abstract
Objective.Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain.Approach.We first present a theoretical model and anin vitrovalidation of the method. We then report the results of anin vivoimplementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes.Main Results.We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike.Significance.Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.
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Affiliation(s)
| | - Duy Ngo
- Department of Statistics, Western Michigan University, Kalamazoo, MI, United States of America
| | - Hernando Ombao
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Joffre E Olaya
- Division of Neurosurgery, Children’s Hospital of Orange County, Orange, CA, United States of America
- Department of Neurosurgery, University of California, Irvine, Irvine, CA, United States of America
| | - Daniel W Shrey
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, United States of America
- Department of Pediatrics, University of California, Irvine, Irvine, CA, United States of America
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States of America
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19
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Saleem A, Santos AC, Aquilino MS, Sivitilli AA, Attisano L, Carlen PL. Modelling hyperexcitability in human cerebral cortical organoids: Oxygen/glucose deprivation most effective stimulant. Heliyon 2023; 9:e14999. [PMID: 37089352 PMCID: PMC10113787 DOI: 10.1016/j.heliyon.2023.e14999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
Epilepsy is a common neurological disorder that affects 1% of the global population. The neonatal period constitutes the highest incidence of seizures. Despite the continual developments in seizure modelling and anti-epileptic drug development, the mechanisms involved in neonatal seizures remain poorly understood. This leaves infants with neonatal seizures at a high risk of death, poor prognosis of recovery and risk of developing neurological disorders later in life. Current in vitro platforms for modelling adult and neonatal epilepsies - namely acute cerebral brain slices or cell-derived cultures, both derived from animals-either lack a complex cytoarchitecture, high-throughput capabilities or physiological similarities to the neonatal human brain. Cerebral organoids, derived from human embryonic stem cells (hESCs), are an emerging technology that could better model neurodevelopmental disorders in the developing human brain. Herein, we study induced hyperexcitability in human cerebral cortical organoids - setting the groundwork for neonatal seizure modelling - using electrophysiological techniques and pharmacological manipulations. In neonatal seizures, energy failure - specifically due to deprivation of oxygen and glucose - is a consistent and reliable seizure induction method that has been used to study the underlying cellular and molecular mechanisms. Here, we applied oxygen-glucose deprivation (OGD) as well as common chemoconvulsants in 3-7-month-old cerebral organoids. Remarkably, OGD resulted in hyperexcitability, with increased power and spontaneous events compared to other common convulsants tested at the population level. These findings characterize OGD as the stimulus most capable of inducing hyperexcitable changes in cerebral organoid tissue, which could be extended to future modelling of neonatal epilepsies in cerebral organoids.
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20
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Lai N, Li Z, Xu C, Wang Y, Chen Z. Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy. Neurobiol Dis 2023; 177:105999. [PMID: 36638892 DOI: 10.1016/j.nbd.2023.105999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.
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Affiliation(s)
- Nanxi Lai
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhisheng Li
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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21
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Attokaren MK, Jeong N, Blanpain L, Paulson AL, Garza KM, Borron B, Walelign M, Willie J, Singer AC. BrainWAVE: A Flexible Method for Noninvasive Stimulation of Brain Rhythms across Species. eNeuro 2023; 10:ENEURO.0257-22.2022. [PMID: 36754625 PMCID: PMC9979148 DOI: 10.1523/eneuro.0257-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 11/23/2022] [Accepted: 12/23/2022] [Indexed: 02/10/2023] Open
Abstract
Rhythmic neural activity, which coordinates brain regions and neurons to achieve multiple brain functions, is impaired in many diseases. Despite the therapeutic potential of driving brain rhythms, methods to noninvasively target deep brain regions are limited. Accordingly, we recently introduced a noninvasive stimulation approach using flickering lights and sounds ("flicker"). Flicker drives rhythmic activity in deep and superficial brain regions. Gamma flicker spurs immune function, clears pathogens, and rescues memory performance in mice with amyloid pathology. Here, we present substantial improvements to this approach that is flexible, user-friendly, and generalizable across multiple experimental settings and species. We present novel open-source methods for flicker stimulation across rodents and humans. We demonstrate rapid, cross-species induction of rhythmic activity without behavioral confounds in multiple settings from electrophysiology to neuroimaging. This flicker approach provides an exceptional opportunity to discover the therapeutic effects of brain rhythms across scales and species.
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Affiliation(s)
- Matthew K Attokaren
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
| | - Nuri Jeong
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA 30322
| | - Lou Blanpain
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA 30322
| | - Abigail L Paulson
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
| | - Kristie M Garza
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA 30322
| | - Ben Borron
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
| | - Michael Walelign
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
| | - Jon Willie
- Neurosurgery, Biomedical Engineering, Psychiatry, Neuroscience and Neurology, Washington University, St Louis, MO 63110
| | - Annabelle C Singer
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA 30322
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22
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Chloride ion dysregulation in epileptogenic neuronal networks. Neurobiol Dis 2023; 177:106000. [PMID: 36638891 DOI: 10.1016/j.nbd.2023.106000] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/25/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
GABA is the major inhibitory neurotransmitter in the mature CNS. When GABAA receptors are activated the membrane potential is driven towards hyperpolarization due to chloride entry into the neuron. However, chloride ion dysregulation that alters the ionic gradient can result in depolarizing GABAergic post-synaptic potentials instead. In this review, we highlight that GABAergic inhibition prevents and restrains focal seizures but then reexamine this notion in the context of evidence that a static and/or a dynamic chloride ion dysregulation, that increases intracellular chloride ion concentrations, promotes epileptiform activity and seizures. To reconcile these findings, we hypothesize that epileptogenic pathologically interconnected neuron (PIN) microcircuits, representing a small minority of neurons, exhibit static chloride dysregulation and should exhibit depolarizing inhibitory post-synaptic potentials (IPSPs). We speculate that chloride ion dysregulation and PIN cluster activation may generate fast ripples and epileptiform spikes as well as initiate the hypersynchronous seizure onset pattern and microseizures. Also, we discuss the genetic, molecular, and cellular players important in chloride dysregulation which regulate epileptogenesis and initiate the low-voltage fast seizure onset pattern. We conclude that chloride dysregulation in neuronal networks appears to be critical for epileptogenesis and seizure genesis, but feed-back and feed-forward inhibitory GABAergic neurotransmission plays an important role in preventing and restraining seizures as well.
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23
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Localization of epileptogenic foci by automatic detection of high‐frequency oscillations based on waveform feature templates. INT J INTELL SYST 2022. [DOI: 10.1002/int.23052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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24
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Zhou Y, You J, Kumar U, Weiss SA, Bragin A, Engel J, Papadelis C, Li L. An approach for reliably identifying high-frequency oscillations and reducing false-positive detections. Epilepsia Open 2022; 7:674-686. [PMID: 36053171 PMCID: PMC9712470 DOI: 10.1002/epi4.12647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/31/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE Aiming to improve the feasibility and reliability of using high-frequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. METHODS We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a time-frequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation. RESULTS The algorithm was run on 768-h recordings of intracranial electrodes in 48 PTE animals. A total of 453 917 HFOs were identified by initial HFO detection, of which 450 917 were implemented for HFO refinement and 203 531 events were retained. Random sampling was used to evaluate the performance of the detector. The HFO detection yielded an overall accuracy of 0.95 ± 0.03 , with precision, recall, and F1 scores of 0.92 ± 0.05 , 0.99 ± 0.01 , and 0.94 ± 0.03 , respectively. For the HFO classification, our algorithm obtained an accuracy of 0.97 ± 0.02 . For the inter-rater reliability of algorithm evaluation, the agreement among four experts was 0.94 ± 0.03 for HFO detection and 0.85 ± 0.04 for HFO classification. SIGNIFICANCE Our approach shows that a segregated pipeline design with a focus on false-positive rejection can improve the detection efficiency and provide reliable results. This pipeline does not require customization and uses fixed parameters, making it highly feasible and translatable for basic and clinical applications of epilepsy.
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Affiliation(s)
- Yufeng Zhou
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA
| | - Jing You
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA
| | - Udaya Kumar
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Shennan A Weiss
- Departments of Neurology, Department of Physiology and PharmacologyState University of New York DownstateBrooklynNew YorkUSA,Department of NeurologyNew York City Health + Hospitals/Kings CountyBrooklynNew YorkUSA
| | - Anatol Bragin
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA,Brain Research InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Jerome Engel
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA,Brain Research InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA,Department of NeurobiologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA,Department of Psychiatry and Biobehavioral SciencesDavid Geffen School of Medicine at UCLACaliforniaUSA
| | - Christos Papadelis
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTexasUSA,School of MedicineTexas Christian UniversityFort WorthTexasUSA,Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTexasUSA
| | - Lin Li
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA,Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
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25
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Curot J, Barbeau E, Despouy E, Denuelle M, Sol JC, Lotterie JA, Valton L, Peyrache A. Local neuronal excitation and global inhibition during epileptic fast ripples in humans. Brain 2022; 146:561-575. [PMID: 36093747 PMCID: PMC9924905 DOI: 10.1093/brain/awac319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 07/01/2022] [Accepted: 08/01/2022] [Indexed: 11/12/2022] Open
Abstract
Understanding the neuronal basis of epileptic activity is a major challenge in neurology. Cellular integration into larger scale networks is all the more challenging. In the local field potential, interictal epileptic discharges can be associated with fast ripples (200-600 Hz), which are a promising marker of the epileptogenic zone. Yet, how neuronal populations in the epileptogenic zone and in healthy tissue are affected by fast ripples remain unclear. Here, we used a novel 'hybrid' macro-micro depth electrode in nine drug-resistant epileptic patients, combining classic depth recording of local field potentials (macro-contacts) and two or three tetrodes (four micro-wires bundled together) enabling up to 15 neurons in local circuits to be simultaneously recorded. We characterized neuronal responses (190 single units) with the timing of fast ripples (2233 fast ripples) on the same hybrid and other electrodes that target other brain regions. Micro-wire recordings reveal signals that are not visible on macro-contacts. While fast ripples detected on the closest macro-contact to the tetrodes were always associated with fast ripples on the tetrodes, 82% of fast ripples detected on tetrodes were associated with detectable fast ripples on the nearest macro-contact. Moreover, neuronal recordings were taken in and outside the epileptogenic zone of implanted epileptic subjects and they revealed an interlay of excitation and inhibition across anatomical scales. While fast ripples were associated with increased neuronal activity in very local circuits only, they were followed by inhibition in large-scale networks (beyond the epileptogenic zone, even in healthy cortex). Neuronal responses to fast ripples were homogeneous in local networks but differed across brain areas. Similarly, post-fast ripple inhibition varied across recording locations and subjects and was shorter than typical inter-fast ripple intervals, suggesting that this inhibition is a fundamental refractory process for the networks. These findings demonstrate that fast ripples engage local and global networks, including healthy tissue, and point to network features that pave the way for new diagnostic and therapeutic strategies. They also reveal how even localized pathological brain dynamics can affect a broad range of cognitive functions.
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Affiliation(s)
- Jonathan Curot
- Correspondence to: Jonathan Curot, MD, PhD CerCo CNRS UMR 5549, Université Toulouse III CHU Purpan, Pavillon Baudot, 31052 Toulouse Cedex, France E-mail:
| | - Emmanuel Barbeau
- Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France,Faculty of Health, University of Toulouse, Paul Sabatier University, Toulouse, France
| | - Elodie Despouy
- Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France
| | - Marie Denuelle
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France
| | - Jean Christophe Sol
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Faculty of Health, University of Toulouse, Paul Sabatier University, Toulouse, France,Toulouse Neuro Imaging Center (ToNIC), INSERM, U1214, Toulouse, France
| | - Jean-Albert Lotterie
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Toulouse Neuro Imaging Center (ToNIC), INSERM, U1214, Toulouse, France
| | - Luc Valton
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France
| | - Adrien Peyrache
- Correspondence may also be addressed to: Adrien Peyrache, PhD Montreal Neurological Institute Department of Neurology and Neurosurgery McGill University, 3810 University Street Montreal, Quebec, Canada E-mail:
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26
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Hassan AR, Zhao Z, Ferrero JJ, Cea C, Jastrzebska‐Perfect P, Myers J, Asman P, Ince NF, McKhann G, Viswanathan A, Sheth SA, Khodagholy D, Gelinas JN. Translational Organic Neural Interface Devices at Single Neuron Resolution. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202306. [PMID: 35908811 PMCID: PMC9507374 DOI: 10.1002/advs.202202306] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Recording from the human brain at the spatiotemporal resolution of action potentials provides critical insight into mechanisms of higher cognitive functions and neuropsychiatric disease that is challenging to derive from animal models. Here, organic materials and conformable electronics are employed to create an integrated neural interface device compatible with minimally invasive neurosurgical procedures and geared toward chronic implantation on the surface of the human brain. Data generated with these devices enable identification and characterization of individual, spatially distribute human cortical neurons in the absence of any tissue penetration (n = 229 single units). Putative single-units are effectively clustered, and found to possess features characteristic of pyramidal cells and interneurons, as well as identifiable microcircuit interactions. Human neurons exhibit consistent phase modulation by oscillatory activity and a variety of population coupling responses. The parameters are furthermore established to optimize the yield and quality of single-unit activity from the cortical surface, enhancing the ability to investigate human neural network mechanisms without breaching the tissue interface and increasing the information that can be safely derived from neurophysiological monitoring.
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Affiliation(s)
- Ahnaf Rashik Hassan
- Institute for Genomic MedicineColumbia University Irving Medical CenterNew YorkNY10032USA
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Zifang Zhao
- Department of Electrical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Jose J. Ferrero
- Institute for Genomic MedicineColumbia University Irving Medical CenterNew YorkNY10032USA
| | - Claudia Cea
- Department of Electrical EngineeringColumbia UniversityNew YorkNY10027USA
| | | | - John Myers
- Department of NeurosurgeryBaylor College of MedicineHoustonTX77030USA
| | - Priscella Asman
- Department of Biomedical EngineeringUniversity of HoustonHoustonTX77004USA
| | - Nuri Firat Ince
- Department of Biomedical EngineeringUniversity of HoustonHoustonTX77004USA
| | - Guy McKhann
- Department of NeurosurgeryColumbia University Irving Medical Center and New York Presbyterian HospitalNew YorkNY10032USA
| | | | - Sameer A. Sheth
- Department of NeurosurgeryBaylor College of MedicineHoustonTX77030USA
| | - Dion Khodagholy
- Department of Electrical EngineeringColumbia UniversityNew YorkNY10027USA
| | - Jennifer N. Gelinas
- Institute for Genomic MedicineColumbia University Irving Medical CenterNew YorkNY10032USA
- Department of NeurologyColumbia University Irving Medical Center and New York Presbyterian HospitalNew YorkNY10032USA
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27
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Lee SH, Thunemann M, Lee K, Cleary DR, Tonsfeldt KJ, Oh H, Azzazy F, Tchoe Y, Bourhis AM, Hossain L, Ro YG, Tanaka A, Kılıç K, Devor A, Dayeh SA. Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces. ADVANCED FUNCTIONAL MATERIALS 2022; 32:2112045. [PMID: 36381629 PMCID: PMC9648634 DOI: 10.1002/adfm.202112045] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Indexed: 05/29/2023]
Abstract
The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain-machine interfaces.
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Affiliation(s)
- Sang Heon Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Martin Thunemann
- Biomedical Engineering Department, Boston University, Boston, MA 02215, USA
| | - Keundong Lee
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Daniel R Cleary
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
- Department of Neurological Surgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Karen J Tonsfeldt
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Science and Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Hongseok Oh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Farid Azzazy
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Youngbin Tchoe
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Andrew M Bourhis
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Lorraine Hossain
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
- Graduate Program of Materials Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Yun Goo Ro
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
| | - Atsunori Tanaka
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
- Graduate Program of Materials Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Kıvılcım Kılıç
- Biomedical Engineering Department, Boston University, Boston, MA 02215, USA
| | - Anna Devor
- Biomedical Engineering Department, Boston University, Boston, MA 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Shadi A Dayeh
- Integrated Electronics and Biointerfaces Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, CA 92093, USA
- Graduate Program of Materials Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
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28
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Bruder JC, Wagner K, Lachner-Piza D, Klotz KA, Schulze-Bonhage A, Jacobs J. Mesial-Temporal Epileptic Ripples Correlate With Verbal Memory Impairment. Front Neurol 2022; 13:876024. [PMID: 35720106 PMCID: PMC9204013 DOI: 10.3389/fneur.2022.876024] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 04/05/2022] [Indexed: 12/03/2022] Open
Abstract
Rationale High frequency oscillations (HFO; ripples = 80–200, fast ripples 200–500 Hz) are promising epileptic biomarkers in patients with epilepsy. However, especially in temporal epilepsies differentiation of epileptic and physiological HFO activity still remains a challenge. Physiological sleep-spindle-ripple formations are known to play a role in slow-wave-sleep memory consolidation. This study aimed to find out if higher rates of mesial-temporal spindle-ripples correlate with good memory performance in epilepsy patients and if surgical removal of spindle-ripple-generating brain tissue correlates with a decline in memory performance. In contrast, we hypothesized that higher rates of overall ripples or ripples associated with interictal epileptic spikes correlate with poor memory performance. Methods Patients with epilepsy implanted with electrodes in mesial-temporal structures, neuropsychological memory testing and subsequent epilepsy surgery were included. Ripples and epileptic spikes were automatically detected in intracranial EEG and sleep-spindles in scalp EEG. The coupling of ripples to spindles was automatically analyzed. Mesial-temporal spindle-ripple rates in the speech-dominant-hemisphere (left in all patients) were correlated with verbal memory test results, whereas ripple rates in the non-speech-dominant hemisphere were correlated with non-verbal memory test performance, using Spearman correlation). Results Intracranial EEG and memory test results from 25 patients could be included. All ripple rates were significantly higher in seizure onset zone channels (p < 0.001). Patients with pre-surgical verbal memory impairment had significantly higher overall ripple rates in left mesial-temporal channels than patients with intact verbal memory (Mann–Whitney-U-Test: p = 0.039). Spearman correlations showed highly significant negative correlations of the pre-surgical verbal memory performance with left mesial-temporal spike associated ripples (rs = −0.458; p = 0.007) and overall ripples (rs = −0.475; p = 0.006). All three ripple types in right-sided mesial-temporal channels did not correlate with pre-surgical nonverbal memory. No correlation for the difference between post- and pre-surgical memory and pre-surgical spindle-ripple rates was seen in patients with left-sided temporal or mesial-temporal surgery. Discussion This study fails to establish a clear link between memory performance and spindle ripples. This highly suggests that spindle-ripples are only a small portion of physiological ripples contributing to memory performance. More importantly, this study indicates that spindle-ripples do not necessarily compromise the predictive value of ripples in patients with temporal epilepsy. The majority of ripples were clearly linked to areas with poor memory function.
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Affiliation(s)
- Jonas Christian Bruder
- Clinic of Neuropediatrics and Muscle Disorders, Freiburg University Medical Center, Breisgau, Germany
- *Correspondence: Jonas Christian Bruder
| | - Kathrin Wagner
- Abteilung Epileptologie Epilepsiezentrum, Klinik Für Neurochirurgie, Universitätsklinikum Freiburg, Breisgau, Germany
| | - Daniel Lachner-Piza
- Clinic of Neuropediatrics and Muscle Disorders, Freiburg University Medical Center, Breisgau, Germany
| | - Kerstin Alexandra Klotz
- Clinic of Neuropediatrics and Muscle Disorders, Freiburg University Medical Center, Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Abteilung Epileptologie Epilepsiezentrum, Klinik Für Neurochirurgie, Universitätsklinikum Freiburg, Breisgau, Germany
| | - Julia Jacobs
- Clinic of Neuropediatrics and Muscle Disorders, Freiburg University Medical Center, Breisgau, Germany
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Tamrakar S, Iimura Y, Suzuki H, Mitsuhashi T, Ueda T, Nishioka K, Karagiozov K, Nakajima M, Miao Y, Tanaka T, Sugano H. Higher phase-amplitude coupling between ripple and slow oscillations indicates the distribution of epileptogenicity in temporal lobe epilepsy with hippocampal sclerosis. Seizure 2022; 100:1-7. [DOI: 10.1016/j.seizure.2022.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 10/18/2022] Open
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Tatum WO. EEG Essentials. Continuum (Minneap Minn) 2022; 28:261-305. [PMID: 35393960 DOI: 10.1212/con.0000000000001129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
PURPOSE OF REVIEW EEG is the best study for evaluating the electrophysiologic function of the brain. The relevance of EEG is based on an accurate interpretation of the recording. Understanding the neuroscientific basis for EEG is essential. The basis for recording and interpreting EEG is both brain site-specific and technique-dependent to detect and represent a complex series of waveforms. Separating normal from abnormal EEG lies at the foundation of essential interpretative skills. RECENT FINDINGS Seizures and epilepsy are the primary targets for clinical use of EEG in diagnosis, seizure classification, and management. Interictal epileptiform discharges on EEG support a clinical diagnosis of seizures, but only when an electrographic seizure is recorded is the diagnosis confirmed. New variations of normal waveforms, benign variants, and artifacts can mimic epileptiform patterns and are potential pitfalls for misinterpretation for inexperienced interpreters. A plethora of medical conditions involve nonepileptiform and epileptiform abnormalities on EEG along the continuum of people who appear healthy to those who are critically ill. Emerging trends in long-term EEG monitoring to diagnose, classify, quantify, and characterize patients with seizures have unveiled epilepsy syndromes in patients and expanded medical and surgical options for treatment. Advances in terminology and application of continuous EEG help unify neurologists in the diagnosis of nonconvulsive seizures and status epilepticus in patients with encephalopathy and prognosticate recovery from serious neurologic injury involving the brain. SUMMARY After 100 years, EEG has retained a key role in the neurologist's toolkit as a safe, widely available, versatile, portable test of neurophysiology, and it is likely to remain at the forefront for patients with neurologic diseases. Interpreting EEG is based on qualitative review, and therefore, the accuracy of reporting is based on the interpreter's training, experience, and exposure to many new and older waveforms.
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Kajikawa S, Matsuhashi M, Kobayashi K, Hitomi T, Daifu-Kobayashi M, Kobayashi T, Yamao Y, Kikuchi T, Yoshida K, Kunieda T, Matsumoto R, Kakita A, Namiki T, Tsuda I, Miyamoto S, Takahashi R, Ikeda A. Two types of clinical ictal direct current shifts in invasive EEG of intractable focal epilepsy identified by waveform cluster analysis. Clin Neurophysiol 2022; 137:113-121. [PMID: 35305495 DOI: 10.1016/j.clinph.2022.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/07/2022] [Accepted: 02/25/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To determine clinically ictal direct current (DC) shifts that can be identified by a time constant (TC) of 2 s and to delineate different types of DC shifts by different attenuation patterns between TC of 10 s and 2 s. METHODS Twenty-one patients who underwent subdural electrode implantation for epilepsy surgery were investigated. For habitual seizures, we compared (1) the peak amplitude and (2) peak latency of the earliest ictal DC shifts between TC of 10 s and 2 s. Cluster and logistic regression analyses were performed based on the attenuation rate of amplitude and peak latency with TC 10 s. RESULTS Ictal DC shifts in 120 seizures were analyzed; 89.1% of which were appropriately depicted even by a TC of 2 s. Cluster and logistic regression analyses revealed two types of ictal DC shift. Namely, a rapid development pattern was defined as the ictal DC shifts with a shorter peak latency and they also showed smaller attenuation rate of amplitude (73/120 seizures). Slow development pattern was defined as the ictal DC shifts with crosscurrent of a rapid development pattern, i.e., a longer peak latency and larger attenuation rate of amplitude (47/120 seizures). Focal cortical dysplasia (FCD) 1A tended to show a rapid development pattern (22/29 seizures) and FCD2A tended to show a slow development pattern (13 /18 seizures), indicating there might be some correlations between two types of ictal DC shift and certain pathologies. CONCLUSIONS Ictal DC shifts, especially rapid development pattern, can be recorded and identified by the AC amplifiers of TC of 2 s which is widely used in many institutes compared to that of TC of 10 s. Two types of ictal DC shifts were identified with possibility of corresponding pathology. SIGNIFICANCE Ictal DC shifts can be distinguished by their attenuation patterns.
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Affiliation(s)
- Shunsuke Kajikawa
- Department of Neurology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Katsuya Kobayashi
- Department of Neurology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Takefumi Hitomi
- Department of Clinical Laboratory, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Masako Daifu-Kobayashi
- Department of Neurology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Tamaki Kobayashi
- Department of Neurosurgery, Otsu City Hospital, 2 Motomiya, Otsu-shi, Shiga 520-0804, Japan; Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Yukihiro Yamao
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Takayuki Kikuchi
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Takeharu Kunieda
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan; Department of Neurosurgery, Ehime University Graduate School of Medicine, 454 Shitsukawa, Touon-shi, Ehime 791-0295, Japan.
| | - Riki Matsumoto
- Department of Neurology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan; Division of Neurology, Kobe University Graduate School of Medicine, 7 Kusunoki-cho, Chuou-ku, Kobe-shi, Hyougo 650-0017, Japan.
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, 757 Asahi-cho 1, Chuou-ku, Niigata-shi, Niigata 951-8585, Japan.
| | - Takao Namiki
- Department of Mathematics, Faculty of Science, Hokkaido University, 8 West, 10 North, Kita-ku, Sapporo-shi, Hokkaido 060-0810, Japan.
| | - Ichiro Tsuda
- Chubu University Academy of Emerging Sciences, Chubu University, 1200 Matsumoto-cho, Kasugai-shi, Aichi 487-8501, Japan.
| | - Susumu Miyamoto
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan.
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Nunez MD, Charupanit K, Sen-Gupta I, Lopour BA, Lin JJ. Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone. J Neural Eng 2022; 19:10.1088/1741-2552/ac520f. [PMID: 35120337 PMCID: PMC9258635 DOI: 10.1088/1741-2552/ac520f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/04/2022] [Indexed: 11/11/2022]
Abstract
Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.
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Affiliation(s)
- Michael D. Nunez
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands,Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Krit Charupanit
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Indranil Sen-Gupta
- Neurology, University of California Irvine Medical Center, Orange CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Jack J. Lin
- Department of Neurology, University of California, Irvine CA, USA
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Paulk AC, Kfir Y, Khanna AR, Mustroph ML, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson RM, Williams ZM, Cash SS. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat Neurosci 2022; 25:252-263. [PMID: 35102333 DOI: 10.1038/s41593-021-00997-0] [Citation(s) in RCA: 91] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/07/2021] [Indexed: 12/20/2022]
Abstract
Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.
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Affiliation(s)
- Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Arjun R Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York City, NY, USA
- Zuckerman Institute, Columbia University, New York City, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York City, NY, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Columbia University, New York City, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sergey D Stavisky
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurological Surgery, University of California at Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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Hadriche A, Behy I, Necibi A, Kachouri A, Amar CB, Jmail N. Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6406362. [PMID: 34992674 PMCID: PMC8727131 DOI: 10.1155/2021/6406362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 11/17/2022]
Abstract
Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique's robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.
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Affiliation(s)
- Abir Hadriche
- REGIM Lab, ENIS, Sfax University, Tunisia
- Digital Research Center of Sfax, Tunisia
| | | | | | | | | | - Nawel Jmail
- MIRACL Lab, Sfax University, Tunisia
- LETI Lab, ENIS, Sfax University, Tunisia
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Prognostic value of high-frequency oscillations combined with multimodal imaging methods for epilepsy surgery. Chin Med J (Engl) 2021; 135:1087-1095. [PMID: 35773966 PMCID: PMC9276102 DOI: 10.1097/cm9.0000000000001909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Background: The combination of high-frequency oscillations (HFOs) with single-mode imaging methods has been proved useful in identifying epileptogenic zones, whereas few studies have examined HFOs combined with multimodal imaging methods. The aim of this study was to evaluate the prognostic value of ripples, an HFO subtype with a frequency of 80 to 200 Hz is combined with multimodal imaging methods in predicting epilepsy surgery outcome. Methods: HFOs were analyzed in 21 consecutive medically refractory epilepsy patients who underwent epilepsy surgery. All patients underwent positron emission tomography (PET) and deep electrode implantation for stereo-electroencephalography (SEEG); 11 patients underwent magnetoencephalography (MEG). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in predicting surgical outcome were calculated for ripples combined with PET, MEG, both PET and MEG, and PET combined with MEG. Kaplan-Meier survival analyses were conducted in each group to estimate prognostic value. Results: The study included 13 men and 8 women. Accuracy for ripples, PET, and MEG alone in predicting surgical outcome was 42.9%, 42.9%, and 81.8%, respectively. Accuracy for ripples combined with PET and MEG was the highest. Resection of regions identified by ripples, MEG dipoles, and combined PET findings was significantly associated with better surgical outcome (P < 0.05). Conclusions: Intracranial electrodes are essential to detect regions which generate ripples and to remove these areas which indicate good surgical outcome for medically intractable epilepsy. With the assistance of presurgical noninvasive imaging examinations, PET and MEG, for example, the SEEG electrodes would identify epileptogenic regions more effectively.
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Righes Marafiga J, Vendramin Pasquetti M, Calcagnotto ME. In vitro Oscillation Patterns Throughout the Hippocampal Formation in a Rodent Model of Epilepsy. Neuroscience 2021; 479:1-21. [PMID: 34710537 DOI: 10.1016/j.neuroscience.2021.10.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 11/29/2022]
Abstract
Specific oscillatory patterns are considered biomarkers of pathological neuronal network in brain diseases, such as epilepsy. However, the dynamics of underlying oscillations during the epileptogenesis throughout the hippocampal formation in the temporal lobe epilepsy is not clear. Here, we characterized in vitro oscillatory patterns within the hippocampal formation of epileptic rats, under 4-aminopyridine (4-AP)-induced hyperexcitability and during the spontaneous network activity, at two periods of epileptogenesis. First, at the beginning of epileptic chronic phase, 30 days post-pilocarpine-induced Status Epilepticus (SE). Second, at the established epilepsy, 60 days post-SE. The 4-AP-bathed slices from epileptic rats had increased susceptibility to ictogenesis in CA1 at 30 days post-SE, and in entorhinal cortex and dentate gyrus at 60 days post-SE. Higher power and phase coherence were detected mainly for gamma and/or high frequency oscillations (HFOs), in a region- and stage-specific manner. Interestingly, under spontaneous network activity, even without 4-AP-induced hyperexcitability, slices from epileptic animals already exhibited higher power of gamma and HFOs in different areas of hippocampal formation at both periods of epileptogenesis, and higher phase coherence in fast ripples at 60 days post-SE. These findings reinforce the critical role of gamma and HFOs in each one of the hippocampal formation areas during ongoing neuropathological processes, tuning the neuronal network to epilepsy.
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Affiliation(s)
- Joseane Righes Marafiga
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, RS, Brazil; Graduate Program in Biological Science: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, RS, Brazil
| | - Mayara Vendramin Pasquetti
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, RS, Brazil; Graduate Program in Biological Science: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, RS, Brazil
| | - Maria Elisa Calcagnotto
- Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory (NNNESP Lab.), Department of Biochemistry, ICBS, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, RS, Brazil; Graduate Program in Biological Science: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, RS, Brazil.
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Yuan H, Li Y, Yang J, Li H, Yang Q, Guo C, Zhu S, Shu X. State of the Art of Non-Invasive Electrode Materials for Brain-Computer Interface. MICROMACHINES 2021; 12:1521. [PMID: 34945371 PMCID: PMC8705666 DOI: 10.3390/mi12121521] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 02/02/2023]
Abstract
The brain-computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.
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Affiliation(s)
- Haowen Yuan
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Yao Li
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Junjun Yang
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Hongjie Li
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Qinya Yang
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Cuiping Guo
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Shenmin Zhu
- State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai 200240, China; (H.Y.); (J.Y.); (H.L.); (Q.Y.); (C.G.); (S.Z.)
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Stangler LA, Kouzani A, Bennet KE, Dumee L, Berk M, Worrell GA, Steele S, Burns TC, Howe CL. Microdialysis and microperfusion electrodes in neurologic disease monitoring. Fluids Barriers CNS 2021; 18:52. [PMID: 34852829 PMCID: PMC8638547 DOI: 10.1186/s12987-021-00292-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/23/2021] [Indexed: 11/10/2022] Open
Abstract
Contemporary biomarker collection techniques in blood and cerebrospinal fluid have to date offered only modest clinical insights into neurologic diseases such as epilepsy and glioma. Conversely, the collection of human electroencephalography (EEG) data has long been the standard of care in these patients, enabling individualized insights for therapy and revealing fundamental principles of human neurophysiology. Increasing interest exists in simultaneously measuring neurochemical biomarkers and electrophysiological data to enhance our understanding of human disease mechanisms. This review compares microdialysis, microperfusion, and implanted EEG probe architectures and performance parameters. Invasive consequences of probe implantation are also investigated along with the functional impact of biofouling. Finally, previously developed microdialysis electrodes and microperfusion electrodes are reviewed in preclinical and clinical settings. Critically, current and precedent microdialysis and microperfusion probes lack the ability to collect neurochemical data that is spatially and temporally coincident with EEG data derived from depth electrodes. This ultimately limits diagnostic and therapeutic progress in epilepsy and glioma research. However, this gap also provides a unique opportunity to create a dual-sensing technology that will provide unprecedented insights into the pathogenic mechanisms of human neurologic disease.
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Affiliation(s)
- Luke A Stangler
- School of Engineering, Deakin University, 3216, Geelong, Victoria, Australia
- Division of Engineering, Mayo Clinic, 55905, Rochester, MN, USA
| | - Abbas Kouzani
- School of Engineering, Deakin University, 3216, Geelong, Victoria, Australia
| | - Kevin E Bennet
- School of Engineering, Deakin University, 3216, Geelong, Victoria, Australia
- Division of Engineering, Mayo Clinic, 55905, Rochester, MN, USA
| | - Ludovic Dumee
- School of Engineering, Deakin University, 3216, Geelong, Victoria, Australia
| | - Michael Berk
- School of Medicine, Deakin University, 3216, Geelong, Victoria, Australia
| | | | - Steven Steele
- Division of Engineering, Mayo Clinic, 55905, Rochester, MN, USA
| | - Terence C Burns
- Department of Neurosurgery, Mayo Clinic, 55905, Rochester, MN, USA
| | - Charles L Howe
- Department of Neurology, Mayo Clinic, 55905, Rochester, MN, USA.
- Division of Experimental Neurology, Mayo Clinic, 55905, Rochester, MN, USA.
- Center for MS and Autoimmune Neurology, Mayo Clinic, 55905, Rochester, MN, USA.
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Guth TA, Kunz L, Brandt A, Dümpelmann M, Klotz KA, Reinacher PC, Schulze-Bonhage A, Jacobs J, Schönberger J. Interictal spikes with and without high-frequency oscillation have different single-neuron correlates. Brain 2021; 144:3078-3088. [PMID: 34343264 PMCID: PMC8634126 DOI: 10.1093/brain/awab288] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 06/07/2021] [Accepted: 07/06/2021] [Indexed: 11/13/2022] Open
Abstract
Interictal epileptiform discharges (IEDs) are a widely used biomarker in patients with epilepsy but lack specificity. It has been proposed that there are truly epileptogenic and less pathological or even protective IEDs. Recent studies suggest that highly pathological IEDs are characterized by high-frequency oscillations (HFOs). Here, we aimed to dissect these 'HFO-IEDs' at the single-neuron level, hypothesizing that the underlying mechanisms are distinct from 'non-HFO-IEDs'. Analysing hybrid depth electrode recordings from patients with temporal lobe epilepsy, we found that single-unit firing rates were higher in HFO- than in non-HFO-IEDs. HFO-IEDs were characterized by a pronounced pre-peak increase in firing, which coincided with the preferential occurrence of HFOs, whereas in non-HFO-IEDs, there was only a mild pre-peak increase followed by a post-peak suppression. Comparing each unit's firing during HFO-IEDs to its baseline activity, we found many neurons with a significant increase during the HFO component or ascending part, but almost none with a decrease. No such imbalance was observed during non-HFO-IEDs. Finally, comparing each unit's firing directly between HFO- and non-HFO-IEDs, we found that most cells had higher rates during HFO-IEDs and, moreover, identified a distinct subset of neurons with a significant preference for this IED subtype. In summary, our study reveals that HFO- and non-HFO-IEDs have different single-unit correlates. In HFO-IEDs, many neurons are moderately activated, and some participate selectively, suggesting that both types of increased firing contribute to highly pathological IEDs.
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Affiliation(s)
- Tim A Guth
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lukas Kunz
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Armin Brandt
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kerstin A Klotz
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter C Reinacher
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Stereotactic and Functional Neurosurgery, Medical Center—University of Freiburg, Freiburg, Germany
- Fraunhofer Institute for Laser Technology, Aachen, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julia Jacobs
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Paediatrics and Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Jan Schönberger
- Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Gliske SV. High frequency oscillations and interictal discharges at 50 μm spatial resolution. Clin Neurophysiol 2021; 132:2894-2895. [PMID: 34563456 PMCID: PMC8675397 DOI: 10.1016/j.clinph.2021.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/29/2021] [Indexed: 11/19/2022]
Affiliation(s)
- Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, USA.
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Jurak P, Cimbalnik J, Klimes P, Daniel P, Brazdil M. Ultra-fast oscillation detection in EEG signal from deep-brain microelectrodes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:265-268. [PMID: 34891287 DOI: 10.1109/embc46164.2021.9629481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
For the last decades, ripples 80-200Hz (R)and fast ripples 200-500Hz (FR) were intensively studied as biomarkers of the epileptogenic zone (EZ). Recently, Very fast ripples 500-1000Hz (VFR) and ultra-fast ripples 1000-2000Hz (UFR) recorded using standard clinical macro electrodes have been shown to be more specific for EZ. High-sampled microelectrode recordings can bring new insights into this phenomenon of high frequency, multiunit activity. Unfortunately, visual detection of such events is extremely time consuming and unreliable. Here we present a detector of ultra-fast oscillations (UFO, >1kHz). In an example of two patients, we detected 951 UFOs which were more frequent in epileptic (8.6/min) vs. non-epileptic hippocampus (1.3/min). Our detection method can serve as a tool for exploring extremely high frequency events from microelectrode recordings.
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Khuvis S, Hwang ST, Mehta AD. Intracranial EEG Biomarkers for Seizure Lateralization in Rapidly-Bisynchronous Epilepsy After Laser Corpus Callosotomy. Front Neurol 2021; 12:696492. [PMID: 34690909 PMCID: PMC8531267 DOI: 10.3389/fneur.2021.696492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci. Methods: We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callosotomy (CC) in three patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA), and low-frequency (filtered) line length (LFLL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG. Results: Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA, and LFLL metrics. Conclusions: While quantitative metrics of IED rate and ictal HGA, PHLG, and LFLL all accurately lateralize based on post-CC iEEG, only IED rate consistently did so based on pre-CC data. Significance: Quantitative analysis of IEDs may be useful in lateralizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis.
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Affiliation(s)
- Simon Khuvis
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States.,Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Sean T Hwang
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States
| | - Ashesh D Mehta
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States.,Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
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Lundstrom BN, Brinkmann BH, Worrell GA. Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes. Brain Commun 2021; 3:fcab231. [PMID: 34704030 PMCID: PMC8536865 DOI: 10.1093/braincomms/fcab231] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/17/2021] [Accepted: 08/31/2021] [Indexed: 11/14/2022] Open
Abstract
Localizing hyperexcitable brain tissue to treat focal seizures remains challenging. We want to identify the seizure onset zone from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Interictal infraslow frequency EEG activity was decreased for seizure onset zone electrodes while faster frequencies such as delta (2-4 Hz) and beta-gamma (20-50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal seizure onset zone electrodes compared to non-seizure onset zone electrodes. Interictal EEG biomarkers correctly classified mesial temporal seizure onset zone electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interictal infraslow EEG activity is decreased near the seizure onset zone while higher frequency power is increased, which may suggest distinct underlying physiologic mechanisms. Narrowband interictal EEG power bands provide information about the seizure onset zone and can help predict mesial temporal involvement in seizure onset. Narrowband interictal EEG power bands may be less useful for predictions related to non-mesial temporal electrodes. Together with interictal epileptiform discharges and high-frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection. Computational modelling suggests changes in neural adaptation may be related to the observed low frequency power changes.
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Yang JC, Paulk AC, Salami P, Lee SH, Ganji M, Soper DJ, Cleary D, Simon M, Maus D, Lee JW, Nahed BV, Jones PS, Cahill DP, Cosgrove GR, Chu CJ, Williams Z, Halgren E, Dayeh S, Cash SS. Microscale dynamics of electrophysiological markers of epilepsy. Clin Neurophysiol 2021; 132:2916-2931. [PMID: 34419344 DOI: 10.1016/j.clinph.2021.06.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics. METHODS Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing. RESULTS We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs. CONCLUSIONS Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network. SIGNIFICANCE These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.
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Affiliation(s)
- Jimmy C Yang
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel Cleary
- Department of Neurosurgery, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Mirela Simon
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Garth Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, 60 Fenwood Rd., Boston, MA 02115, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
| | - Eric Halgren
- Department of Radiology, University of California, San Diego; 9500 Gilman Dr.; La Jolla, CA 92093, USA
| | - Shadi Dayeh
- Department of Electrical and Computer Engineering, University of California, San Diego; 9500 Gilman Dr., La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA.
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Chiang CH, Wang C, Barth K, Rahimpour S, Trumpis M, Duraivel S, Rachinskiy I, Dubey A, Wingel KE, Wong M, Witham NS, Odell T, Woods V, Bent B, Doyle W, Friedman D, Bihler E, Reiche CF, Southwell DG, Haglund MM, Friedman AH, Lad SP, Devore S, Devinsky O, Solzbacher F, Pesaran B, Cogan G, Viventi J. Flexible, high-resolution thin-film electrodes for human and animal neural research. J Neural Eng 2021; 18:10.1088/1741-2552/ac02dc. [PMID: 34010815 PMCID: PMC8496685 DOI: 10.1088/1741-2552/ac02dc] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 05/19/2021] [Indexed: 11/11/2022]
Abstract
Objective.Brain functions such as perception, motor control, learning, and memory arise from the coordinated activity of neuronal assemblies distributed across multiple brain regions. While major progress has been made in understanding the function of individual neurons, circuit interactions remain poorly understood. A fundamental obstacle to deciphering circuit interactions is the limited availability of research tools to observe and manipulate the activity of large, distributed neuronal populations in humans. Here we describe the development, validation, and dissemination of flexible, high-resolution, thin-film (TF) electrodes for recording neural activity in animals and humans.Approach.We leveraged standard flexible printed-circuit manufacturing processes to build high-resolution TF electrode arrays. We used biocompatible materials to form the substrate (liquid crystal polymer; LCP), metals (Au, PtIr, and Pd), molding (medical-grade silicone), and 3D-printed housing (nylon). We designed a custom, miniaturized, digitizing headstage to reduce the number of cables required to connect to the acquisition system and reduce the distance between the electrodes and the amplifiers. A custom mechanical system enabled the electrodes and headstages to be pre-assembled prior to sterilization, minimizing the setup time required in the operating room. PtIr electrode coatings lowered impedance and enabled stimulation. High-volume, commercial manufacturing enables cost-effective production of LCP-TF electrodes in large quantities.Main Results. Our LCP-TF arrays achieve 25× higher electrode density, 20× higher channel count, and 11× reduced stiffness than conventional clinical electrodes. We validated our LCP-TF electrodes in multiple human intraoperative recording sessions and have disseminated this technology to >10 research groups. Using these arrays, we have observed high-frequency neural activity with sub-millimeter resolution.Significance.Our LCP-TF electrodes will advance human neuroscience research and improve clinical care by enabling broad access to transformative, high-resolution electrode arrays.
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Affiliation(s)
- Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- These authors contributed equally to this work
| | - Charles Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- These authors contributed equally to this work
| | - Katrina Barth
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Shervin Rahimpour
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
| | - Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | | | - Iakov Rachinskiy
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Agrita Dubey
- Center for Neural Science, New York University, NY, NY, United States of America
| | - Katie E Wingel
- Center for Neural Science, New York University, NY, NY, United States of America
| | - Megan Wong
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Nicholas S Witham
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States of America
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America
| | - Thomas Odell
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America
| | - Virginia Woods
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Werner Doyle
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, United States of America
| | - Daniel Friedman
- Department of Neurology, NYU Grossman School of Medicine, NY, NY, United States of America
| | | | - Christopher F Reiche
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States of America
| | - Derek G Southwell
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
| | - Michael M Haglund
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
| | - Allan H Friedman
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
| | - Shivanand P Lad
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
| | - Sasha Devore
- Department of Neurology, NYU Grossman School of Medicine, NY, NY, United States of America
| | - Orrin Devinsky
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, United States of America
- Department of Neurology, NYU Grossman School of Medicine, NY, NY, United States of America
- Comprehensive Epilepsy Center, NYU Langone Health, NY, NY, United States of America
| | - Florian Solzbacher
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, United States of America
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America
- Department of Materials Science & Engineering, University of Utah, Salt Lake City, UT, United States of America
| | - Bijan Pesaran
- Center for Neural Science, New York University, NY, NY, United States of America
- Department of Neurology, NYU Grossman School of Medicine, NY, NY, United States of America
| | - Gregory Cogan
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States of America
- Center for Cognitive Neuroscience, Duke University, Durham, NC, United States of America
- Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC, United States of America
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke School of Medicine, Durham, NC, United States of America
- Department of Neurobiology, Duke School of Medicine, Durham, NC, United States of America
- Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC, United States of America
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Nadalin JK, Eden UT, Han X, Richardson RM, Chu CJ, Kramer MA. Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram. J Neurosci Methods 2021; 360:109239. [PMID: 34090917 DOI: 10.1016/j.jneumeth.2021.109239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 05/17/2021] [Accepted: 05/30/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND A reliable biomarker to identify cortical tissue responsible for generating epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike ripple events are a promising biomarker for epileptogenic tissue that currently require expert review for accurate identification. This expert review is time consuming and subjective, limiting reproducibility and high-throughput applications. NEW METHOD To address this limitation, we develop a fully-automated method for spike ripple detection. The method consists of a convolutional neural network trained to compute the probability that a spectrogram image contains a spike ripple. RESULTS We validate the proposed spike ripple detector on expert-labeled data and show that this detector accurately separates subjects with low and high seizure risks. COMPARISON WITH EXISTING METHOD The proposed method performs as well as existing methods that require manual validation of candidate spike ripple events. The introduction of a fully automated method reduces subjectivity and increases rigor and reproducibility of this epilepsy biomarker. CONCLUSION We introduce and validate a fully-automated spike ripple detector to support utilization of this epilepsy biomarker in clinical and translational work.
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Affiliation(s)
- Jessica K Nadalin
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States
| | - Xue Han
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States; Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States.
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Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources. Proc Natl Acad Sci U S A 2021; 118:2011130118. [PMID: 33875582 PMCID: PMC8092606 DOI: 10.1073/pnas.2011130118] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci. High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.
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Tamim I, Chung DY, de Morais AL, Loonen ICM, Qin T, Misra A, Schlunk F, Endres M, Schiff SJ, Ayata C. Spreading depression as an innate antiseizure mechanism. Nat Commun 2021; 12:2206. [PMID: 33850125 PMCID: PMC8044138 DOI: 10.1038/s41467-021-22464-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 03/15/2021] [Indexed: 12/16/2022] Open
Abstract
Spreading depression (SD) is an intense and prolonged depolarization in the central nervous systems from insect to man. It is implicated in neurological disorders such as migraine and brain injury. Here, using an in vivo mouse model of focal neocortical seizures, we show that SD may be a fundamental defense against seizures. Seizures induced by topical 4-aminopyridine, penicillin or bicuculline, or systemic kainic acid, culminated in SDs at a variable rate. Greater seizure power and area of recruitment predicted SD. Once triggered, SD immediately suppressed the seizure. Optogenetic or KCl-induced SDs had similar antiseizure effect sustained for more than 30 min. Conversely, pharmacologically inhibiting SD occurrence during a focal seizure facilitated seizure generalization. Altogether, our data indicate that seizures trigger SD, which then terminates the seizure and prevents its generalization.
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Affiliation(s)
- Isra Tamim
- Neurovascular Research Unit, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Charité-Universitätsmedizin Berlin, Klinik und Hochschulambulanz für Neurologie und Centrum für Schlaganfallforschung Berlin (CSB), Berlin, Germany
| | - David Y Chung
- Neurovascular Research Unit, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andreia Lopes de Morais
- Neurovascular Research Unit, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Inge C M Loonen
- Neurovascular Research Unit, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tao Qin
- Neurovascular Research Unit, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Amrit Misra
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Frieder Schlunk
- Charité-Universitätsmedizin Berlin, Klinik und Hochschulambulanz für Neurologie und Centrum für Schlaganfallforschung Berlin (CSB), Berlin, Germany
| | - Matthias Endres
- Charité-Universitätsmedizin Berlin, Klinik und Hochschulambulanz für Neurologie und Centrum für Schlaganfallforschung Berlin (CSB), Berlin, Germany
| | - Steven J Schiff
- Center for Neural Engineering, Departments of Engineering Science and Mechanics and Physics, The Pennsylvania State University, State College, PA, USA
| | - Cenk Ayata
- Neurovascular Research Unit, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Agudelo Valencia P, van Klink NEC, van 't Klooster MA, Zweiphenning WJEM, Swampillai B, van Eijsden P, Gebbink T, van Zandvoort MJE, Zijlmans M. Are HFOs in the Intra-operative ECoG Related to Hippocampal Sclerosis, Volume and IQ? Front Neurol 2021; 12:645925. [PMID: 33841312 PMCID: PMC8024640 DOI: 10.3389/fneur.2021.645925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/02/2021] [Indexed: 11/13/2022] Open
Abstract
Temporal lobe epilepsy (TLE) is the most common form of refractory focal epilepsy and is often associated with hippocampal sclerosis (HS) and cognitive disturbances. Over the last decade, high frequency oscillations (HFOs) in the intraoperative electrocorticography (ioECoG) have been proposed to be biomarkers for the delineation of epileptic tissue but hippocampal ripples have also been associated with memory consolidation. Healthy hippocampi can show prolonged ripple activity in stereo- EEG. We aimed to identify how the HFO rates [ripples (80–250 Hz, fast ripples (250–500 Hz); prolonged ripples (80–250 Hz, 200–500 ms)] in the pre-resection ioECoG over subtemporal area (hippocampus) and lateral temporal neocortex relate to presence of hippocampal sclerosis, the hippocampal volume quantified on MRI and the severity of cognitive impairment in TLE patients. Volumetric measurement of hippocampal subregions was performed in 47 patients with TLE, who underwent ioECoG. Ripples, prolonged ripples, and fast ripples were visually marked and rates of HFOs were calculated. The intellectual quotient (IQ) before resection was determined. There was a trend toward higher rates of ripples and fast ripples in subtemporal electrodes vs. the lateral neocortex (ripples: 2.1 vs. 1.3/min; fast ripples: 0.9 vs. 0.2/min). Patients with HS showed higher rates of subtemporal fast ripples than other patients (Z = −2.51, p = 0.012). Prolonged ripples were only found in the lateral temporal neocortex. The normalized ratio (smallest/largest) of hippocampal volume was correlated to pre-resection IQ (r = 0.45, p = 0.015). There was no correlation between HFO rates and hippocampal volumes or HFO rates and IQ. To conclude, intra-operative fast ripples were a marker for HS, but ripples and fast ripples were not linearly correlated with either the amount of hippocampal atrophy, nor for pre-surgical IQ.
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Affiliation(s)
- Paula Agudelo Valencia
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Nicole E C van Klink
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Maryse A van 't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Willemiek J E M Zweiphenning
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Banu Swampillai
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Pieter van Eijsden
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Tineke Gebbink
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Martine J E van Zandvoort
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, Netherlands.,Stiching Epilepsie Instellingen Nederland (SEIN), Heemstede, Netherlands
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Kuroda N, Sonoda M, Miyakoshi M, Nariai H, Jeong JW, Motoi H, Luat AF, Sood S, Asano E. Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome. Brain Commun 2021; 3:fcab042. [PMID: 33959709 PMCID: PMC8088817 DOI: 10.1093/braincomms/fcab042] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/09/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving post-operative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3–4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across non-epileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the non-epileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of post-operative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.
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Affiliation(s)
- Naoto Kuroda
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Masaki Sonoda
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurosurgery, Yokohama City University, Yokohama 2360004, Japan
| | - Makoto Miyakoshi
- Swartz Centre for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
| | - Hiroki Nariai
- Division of Paediatric Neurology, Department of Paediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jeong-Won Jeong
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Hirotaka Motoi
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Paediatrics, Yokohama City University Medical Centre, Yokohama 2320024, Japan
| | - Aimee F Luat
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
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